31 citations · 47 across the 6 of their papers we have counts for
8 papers
Iterative Data Curation with Theoretical Guarantees
Väinö Yrjänäinen, Johan Jonasson, Måns Magnusson
In recent years, more and more large data sets have become available. Data accuracy, the absence of verifiable errors in data, is crucial for these large materials to enable high-q…
Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation
Hannes Waldetoft, Jakob Torgander, Måns Magnusson
Estimating population parameters in finite populations of text documents can be challenging when obtaining the labels for the target variable requires manual annotation. To address…
An Image is Worth Topics: A Visual Structural Topic Model with Pretrained Image Embeddings
Matías Piqueras, Alexandra Segerberg, Matteo Magnani +2
Political scientists are increasingly interested in analyzing visual content at scale. However, the existing computational toolbox is still in need of methods and models attuned to…
Frequentist Oracle Properties of Bayesian Stacking Estimators
Valentin Zulj, Shaobo Jin, Måns Magnusson
Compromise estimation entails using a weighted average of outputs from several candidate models, and is a viable alternative to model selection when the choice of model is not obvi…
The Cambridge Law Corpus: A Dataset for Legal AI Research
Andreas Östling, Holli Sargeant, Huiyuan Xie +5
We introduce the Cambridge Law Corpus (CLC), a dataset for legal AI research. It consists of over 250 000 court cases from the UK. Most cases are from the 21st century, but the cor…
Robust, Accurate Stochastic Optimization for Variational Inference
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen +3
We consider the problem of fitting variational posterior approximations using stochastic optimization methods. The performance of these approximations depends on (1) how well the v…
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data
Måns Magnusson, Michael Riis Andersen, Johan Jonasson +1
Recently, new methods for model assessment, based on subsampling and posterior approximations, have been proposed for scaling leave-one-out cross-validation (LOO) to large datasets…
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
Alexander Terenin, Måns Magnusson, Leif Jonsson
To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distribute…